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Justin Grimmer
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Margaret E. Roberts
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Brandon M. Stewart
pubblicato da
Princeton University Press
A guide for using computational text analysis to learn about the social world
From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted. Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights.
Text as Data is organized around the core tasks in research projects using textrepresentation, discovery, measurement, prediction, and causal inference. The authors offer a sequential, iterative, and inductive approach to research design. Each research task is presented complete with real-world applications, example methods, and a distinct style of task-focused research.
Bridging many dividescomputer science and social science, the qualitative and the quantitative, and industry and academiaText as Data is an ideal resource for anyone wanting to analyze large collections of text in an era when data is abundant and computation is cheap, but the enduring challenges of social science remain.
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